Reconstructing thawing quintessence with multiple datasets

TL;DR

This paper models thawing quintessence using a Taylor series expansion of the potential and constrains it with multiple cosmological datasets, finding modest improvements over past constraints.

Contribution

It introduces a second-order Taylor expansion of the quintessence potential and combines various datasets to constrain the model parameters.

Findings

Supernova data provides the strongest constraints.

Growth data is competitive with other datasets.

Combined constraints show only modest improvement over past results.

Abstract

In this work we model the quintessence potential in a Taylor series expansion, up to second order, around the present-day value of the scalar field. The field is evolved in a thawing regime assuming zero initial velocity. We use the latest data from the Planck satellite, baryonic acoustic oscillations observations from the Sloan Digital Sky Survey, and Supernovae luminosity distance information from Union2.1 to constrain our models parameters, and also include perturbation growth data from the WiggleZ, BOSS and the 6dF surveys. The supernova data provide the strongest individual constraint on the potential parameters. We show that the growth data performance is competitive with the other datasets in constraining the dark energy parameters we introduce. We also conclude that the combined constraints we obtain for our model parameters, when compared to previous works of nearly a decade ago, have shown only modest improvement, even with new growth of structure data added to previously-existent types of data.